Expert and novice sensitivity to environmental regularities in predicting NFL games
We study whether experts and novices differ in the way they make predictions about National Football League games. In particular, we measure to what extent their predictions are consistent with five environmental regularities that could support decision making based on heuristics. These regularities...
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| Auteurs principaux: | , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Society for Judgment and Decision Making
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/fba3f212d85b40b6be6fbf2ec8be7e5d |
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| Résumé: | We study whether experts
and novices differ in the way they make predictions about National Football
League games. In particular, we measure to what extent their predictions are
consistent with five environmental regularities that could support decision
making based on heuristics. These regularities involve the home team winning
more often, the team with the better win-loss record winning more often, the
team favored by the majority of media experts winning more often, and two
others related to surprise wins and losses in the teams’ previous game. Using
signal detection theory and hierarchical Bayesian analysis, we show that expert
predictions for the 2017 National Football League (NFL) season generally follow
these regularities in a near optimal way, but novice predictions do not. These
results support the idea that using heuristics adapted to the decision
environment can support accurate predictions and be an indicator of
expertise. |
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